Division of Healthcare and Life Sciences, IBM Research, Armonk, New York, USA.
MIT-IBM Watson AI Lab, Cambridge, Massachusetts, USA.
Pharmacoepidemiol Drug Saf. 2022 Sep;31(9):944-952. doi: 10.1002/pds.5491. Epub 2022 Jun 21.
With availability of voluminous sets of observational data, an empirical paradigm to screen for drug repurposing opportunities (i.e., beneficial effects of drugs on nonindicated outcomes) is feasible. In this article, we use a linked claims and electronic health record database to comprehensively explore repurposing effects of antihypertensive drugs. We follow a target trial emulation framework for causal inference to emulate randomized controlled trials estimating confounding adjusted effects of antihypertensives on each of 262 outcomes of interest. We then fit hierarchical models to the results as a form of postprocessing to account for multiple comparisons and to sift through the results in a principled way. Our motivation is twofold. We seek both to surface genuinely intriguing drug repurposing opportunities and to elucidate through a real application some study design decisions and potential biases that arise in this context.
随着大量观察性数据集的出现，筛选药物再利用机会（即药物对非适应证结果的有益作用）的经验范式是可行的。在本文中，我们使用链接的索赔和电子健康记录数据库来全面探索降压药的再利用效果。我们遵循目标试验模拟框架进行因果推理，模拟随机对照试验，估计降压药对 262 个感兴趣的结果中的每一个的混杂调整效果。然后，我们将层次模型拟合到结果中，作为一种后处理形式，以考虑多次比较，并以一种有原则的方式筛选结果。我们的动机有两个。我们既希望发现真正有趣的药物再利用机会，又希望通过实际应用阐明在这种情况下出现的一些研究设计决策和潜在偏差。
